Building a DeFi Yield Scanner with Python and AI

DeFi yields are volatile, fragmented, and often deceptive. A static list of APYs is useless in a market where rates shift every block. To navigate this, you need a dynamic Yield Scanner that doesn't just fetch data but interprets it. By combining Python’s data processing power with AI-driven anomaly detection, you can build a system that filters out "rug pull" risks and highlights sustainable yields.

Here is how to architect the core data pipeline. First, we need a robust scraper to aggregate data from major aggregators like DeFiLlama. We use requests for efficiency and pandas for immediate normalization.

import requests
import pandas as pd

def fetch_yield_data():
    url = "https://yields.llama.fi/pools"
    response = requests.get(url)
    data = response.json()['data']

    # Convert to DataFrame for easy manipulation
    df = pd.DataFrame(data)

    # Filter for major chains to reduce noise
    df = df[df['chain'].isin(['Ethereum', 'Arbitrum', 'Optimism'])]

    return df[['project', 'chain', 'tvlUsd', 'apyBase', 'apyReward']]

Raw APY is a trap. A 500% yield often implies high inflationary rewards or extreme volatility. This is where AI enters the stack. Instead of writing complex statistical models from scratch, leverage an external AI API to analyze historical patterns and sentiment. You can send a summarized snapshot of a specific pool’s history to an LLM endpoint to get a risk score.

python
import json

def assess_risk_with_ai(pool_data, api_key):
    prompt = f"""
    Analyze this DeFi pool: {json.dumps(pool_data)}
    Current APY: {pool_data['apyBase']}%
    TVL: ${pool_data['tvlUsd']}

    Provide a risk score (1-10) and a one-sentence justification 
    focusing on sustainability and smart contract risks.
    """

    # Hypothetical AI API call
    # response = ai_client.chat.completions.create(
    #     model="gpt-4o-mini",
    #     messages=[{"role": "user", "content": prompt}],
    #     api_key=api_key